Elective surgery waiting time prioritisation to improve population health gains and reduce health inequalities

Project theme: Applied economic evaluation and policy analysis

This is a joint project with the Economics of Health Systems and Interface with Social Care Policy Research Unit (ESHCRU). Since the start of the COVID-19 pandemic, elective activity in NHS hospitals has reduced substantially due to patients avoiding care and hospitals cancelling scheduled treatments to create capacity for the rapid surge in COVID-19 patients. With elective activity still below pre-pandemic levels, the backlog of patients with unmet needs is growing constantly and, as a result, waiting lists have reached historically high levels [1].

Traditionally, the English NHS has operated a universal waiting time target of 18 weeks from referral to surgery for most elective surgeries (with a few exceptions such as cancer surgery). This approach fails to reflect the different health effects and opportunity costs of delaying different types of elective care. If patient populations differ in the benefits they obtain from more rapid access to elective surgeries (e.g. due to disease progression / irreversibilities), a targeted waiting time policy that sets different maximum waiting times for different elective interventions can improve population health and help the NHS ‘to build back better’.

Two recent studies have examined approaches for prioritising patients for elective surgeries [2,3]. Gravesteijn et al (2021) modelled the health consequences, measured in disability-adjusted life-years (DALYs), of delaying 43 types of elective surgeries in the Netherlands during the COVID-19 pandemic [3]. The impact of varying delays for each procedure was estimated using a common model reflecting the impacts of surgery on survival and quality of life pre and post-surgery. The analysis did not consider resource requirements of surgery or the best approach to prioritise surgery. D’Aeth et al (2021) proposed a linear programming approach to optimise schedules in order to minimise years of life lost in England [2]. The model captures the impact of COVID-19 on capacity and the availability of resources for elective care and used historical data to estimate demand. The negative impact of delays in elective care are captured by a risk of progression resulting in emergency care. However, the study only captured impact on life years and thus fails to reflect the impacts of delays on morbidity. Neither study considered the impacts on inequalities.

Our research will aim to examine the relationship between waiting times and health outcomes in terms of quality-adjusted life-years (QALYs) for different elective surgeries conditional on the characteristics of the patients (including equity-relevant characteristics). i Using this evidence we will explore alternative prioritisation strategies for tackling waiting lists considering the dual objectives of maximising population health and reducing health inequalities.

Aims

This research aims to inform efficient referral-to-treatment (RTT) targets for types of elective surgery by:

i) Estimating the current backlog in elective surgery

ii) Estimating how variation in waiting time across forms of elective surgery has changed because of the pandemic

iii) Quantifying the health consequences of waiting by condition in terms of QALYs and describe this alongside the characteristics of the patient population for each condition (including equity relevant characteristics)

iv) Explore the impacts of alternative prioritisation strategies for tackling waiting lists on population health and health inequalities

Project Team

Simon Walker, Nils Gutacker (ESCHRU), Susan Griffin and Naomi Gibb

Contact

Simon Walker

simon.walker@york.ac.uk

References

1. Griffin S. COVID-19: waiting times in England reach record highs. BMJ. 2020;370:m3557.

2. D’Aeth JC, Ghosal S, Grimm F, Haw D, Koca E, Lau K, et al. Optimal national prioritization policies for hospital care during the SARS-CoV-2 pandemic. Nat Comput Sci. 2021;1–11. Available from: https://www.nature.com/articles/s43588-021-00111-1

3. Gravesteijn B, Krijkamp E, Busschbach J, Geleijnse G, Helmrich IR, Bruinsma S, et al. Minimizing Population Health Loss in Times of Scarce Surgical Capacity During the Coronavirus Disease 2019 Crisis and Beyond: A Modeling Study. Value in Health. 2021;24:648–57. Available from: http://www.valueinhealthjournal.com/article/S1098301521000462/fulltext